Deciphering Data

To outsiders, data might seem pretty simple. Data is stored and then someone tries to make sense of it. Sounds pretty simple right? Working in the data industry, I think it is far more.

The huge amount of work that goes into managing, maintaining, extracting and analysing data to make any real sense of it is a science. It is not just something that any thirteen-year-old could do. The managing and maintaining of data alone is a multi-million-pound industry, and that is just the beginning.

The deeper one goes, the more complex and intriguing data and its various purposes and functions get.

Data can be interpreted in a number of ways depending on its purpose. These countless methods allow for increased innovation in how it can be manipulated and stored, thus making the data almost natural in how it can evolve and develop.

Let me explain why I think this:

Take the data trends. It starts with pure data and looking at how it could be stored effectively, to then being used to understand company trends and then create predictive models. Data can even create cost-effective business plans that now lie at the heart of many business models.

Looking ahead at the industry future, the growing trend of using data towards Artificial Intelligence (AI) looks like the logical next step. The ability to let a machine do all of the heavy-lifting and provide solid and regular reports is all the craze currently. The obsession with creating human-like robots, moving backwards and forwards similarly to that which humans while jumping from one programmed function to the next.

I believe AI is so vital to many businesses. It can be seen as a quick and manageable way to manipulate data and then provide the relevant information. In my opinion, the further AI can be taken, the better. This is due to the untapped potential that data has, but also the world has for AI to operate and perform in.

One aspect of AI which is another current industry trend, in my opinion, is the interesting subject of Deep Learning. This is the concept and in very few real-world examples, of a machine that can adapt and process complex data tasks without human input or control. The ideology, and now practice that may have seemed in the realms of science fiction, is now a reality- but how far can we take deep learning?

Finally, with much in the news in recent months about data uses and permissions, I want to touch on the ethics of data and its relation to politics.

As a person with a substantial interest in politics, I can understand the reasons why personal data needs to be protected. It doesn’t take a mastermind to work that out. The next big question here I feel is how in an ever-changing data-landscape can these guidelines be followed by absolutely everybody?

For example, take Facebook’s recent data scandal. As one of the world’s largest companies, they were recently accused of mishandling the public’s data. Once anyone agrees to a company’s terms and conditions, you are at their mercy in terms of what they do with that data. Having paid very close attention to the congress hearing of Mark Zuckerberg, Facebook’s famous founder, I was astounded that some form of an expert was not on hand to assist the committee investigating the case into understanding the intricate details of the problem that had arisen.

I know that all involved parties would have given many briefings and accounts explaining their perspectives and would have attended several meetings so that issue was understood fully. It seemed to me that the committee was clueless on the matter at hand and that Facebook’s founder was made to explain the basics to them whilst being under investigation.

Overall the world of data is forever changing. This is why I am so intrigued by it. With countless innovations and advancements across several sectors, there really is something new going on all the time. It relates strongly to a philosophy that I live by - “a day where you’ve not learnt anything is a day wasted.”